{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/huangzhiming/Documents/math_readproof_work-_ipython_files/2024'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %load \"../environment.py\"\n",
    "# You must import sympy.abc first then import sympy\n",
    "# or it will have some bugs when it run solvers for \n",
    "# trigeometric functions.\n",
    "from sympy.abc import x,y,z,t,k,l,n,m,p,q\n",
    "from sympy import *\n",
    "from sympy.calculus.util import *\n",
    "from sympy.stats import *\n",
    "\n",
    "from sys import path,platform\n",
    "match platform:\n",
    "    case \"linux\":\n",
    "        path.append(\"/home/huang/Documents/packaging_tutorial/src\")\n",
    "    case \"darwin\":\n",
    "        path.append(\"/Users/huangzhiming/Documents/python学习/packaging_tutorial/src\")\n",
    "%load_ext autoreload \n",
    "%autoreload 1\n",
    "\n",
    "%aimport basic_package.utils\n",
    "%aimport function_calculator_package.extreme_points\n",
    "%aimport function_calculator_package.utils\n",
    "%aimport quadratic_function.utils\n",
    "%aimport quadratic_function.hyperbola\n",
    "%aimport quadratic_function.utils\n",
    "\n",
    "from basic_package.utils import *\n",
    "from function_calculator_package.extreme_points import *\n",
    "from function_calculator_package.utils import *\n",
    "from quadratic_function.quadraticfunction import QuadraticFunction\n",
    "from quadratic_function.hyperbola import Hyperbola\n",
    "from quadratic_function.utils import line_and_quadratic\n",
    "\n",
    "%aimport excel_function_package.chi_square_test\n",
    "from excel_function_package.chi_square_test import ChiSquaredTest,chi_squared_of_error_probability\n",
    "%aimport excel_function_package.data\n",
    "\n",
    "from excel_function_package.data import Data\n",
    "\n",
    "%aimport solver.utils\n",
    "from solver.utils import solve_univariate_inequalities\n",
    "\n",
    "%aimport function_calculator_package.utils \n",
    "from function_calculator_package.utils import function_is_odd,function_is_even\n",
    "\n",
    "%aimport geometry3D.frustum\n",
    "%aimport geometry3D.prism\n",
    "%aimport geometry3D.pyramid\n",
    "%aimport geometry3D.frustum_cone\n",
    "%aimport geometry3D.sphere\n",
    "\n",
    "from geometry3D.frustum import Frustum\n",
    "from geometry3D.prism import Prism,Cube,Cylinder\n",
    "from geometry3D.pyramid import Pyramid,Cone,Tetrahedron\n",
    "from geometry3D.frustum_cone import FrustumCone\n",
    "from geometry3D.sphere import Sphere\n",
    "a,b,c=symbols(\"a,b,c\",real=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a,q=symbols(\"a,q\",real=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "seq=sequence(a*q**(n-1),(n,1,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 5$"
      ],
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq.length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left\\{\\frac{5}{31}\\right\\}$"
      ],
      "text/plain": [
       "{5/31}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solveset(sum(seq).subs(q,2)-5,a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{35}{31}$"
      ],
      "text/plain": [
       "35/31"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(seq[:3]).subs({a:5/S(31),q:2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{5 \\cdot 2^{n + 1}}{62} - \\frac{5}{31}$"
      ],
      "text/plain": [
       "5*2**(n + 1)/62 - 5/31"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summation(5/S(31)*(2)**(n-1),(n,1,n))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{5 \\cdot \\left(2^{n} - 621\\right)}{31}$"
      ],
      "text/plain": [
       "5*(2**n - 621)/31"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "factor(_-100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{2555}{31}$"
      ],
      "text/plain": [
       "2555/31"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "summation(5/S(31)*(2)**(n-1),(n,1,9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "d=symbols(\"d\",real=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "seq=sequence(a+(n-1)*d,(n,1,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left\\{\\left( \\frac{4}{3}, \\  - \\frac{1}{6}\\right)\\right\\}$"
      ],
      "text/plain": [
       "{(4/3, -1/6)}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linsolve([sum(seq[:2])-sum(seq[2:]),sum(seq)-5],[a,d])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "seq=sequence(64+7*(n-1),(n,1,oo))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[64, 71, 78, 85, \\ldots\\right]$"
      ],
      "text/plain": [
       "SeqFormula(7*n + 57, (n, 1, oo))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left\\{- \\frac{233}{7}, 16\\right\\}$"
      ],
      "text/plain": [
       "{-233/7, 16}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solveset(summation(64+7*(n-1),(n,1,n))-1864,n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "seq=sequence(summation(64+7*(n-1),(n,1,n))*3,(n,1,16))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{7 n^{2}}{2} + \\frac{121 n}{2}$"
      ],
      "text/plain": [
       "7*n**2/2 + 121*n/2"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summation(64+7*(n-1),(n,1,n))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 3300$"
      ],
      "text/plain": [
       "3300"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(seq[:5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 2124$"
      ],
      "text/plain": [
       "2124"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq.coeff(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{573}{2}$"
      ],
      "text/plain": [
       "573/2"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq.coeff(10)/10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 40392$"
      ],
      "text/plain": [
       "40392"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summation(_,(n,1,16))*3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{7 n^{3}}{2} + 96 n^{2} + \\frac{185 n}{2}$"
      ],
      "text/plain": [
       "7*n**3/2 + 96*n**2 + 185*n/2"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summation(summation(64+7*(n-1),(n,1,n))*3,(n,1,n))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{n \\left(n + 1\\right) \\left(7 n + 185\\right)}{2}$"
      ],
      "text/plain": [
       "n*(n + 1)*(7*n + 185)/2"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "factor(_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13464.0"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "40392/3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "seq=sequence(a+17*(n-1),(n,1,8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[a, a + 17, a + 34, a + 51, \\ldots\\right]$"
      ],
      "text/plain": [
       "SeqFormula(a + 17*n - 17, (n, 1, 8))"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left\\{65\\right\\}$"
      ],
      "text/plain": [
       "{65}"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solveset(sum(seq)-996,a)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left[65, 82, 99, 116, \\ldots\\right]$"
      ],
      "text/plain": [
       "SeqFormula(17*n + 48, (n, 1, 8))"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seq.subs(a,65)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "65\n",
      "82\n",
      "99\n",
      "116\n",
      "133\n",
      "150\n",
      "167\n",
      "184\n"
     ]
    }
   ],
   "source": [
    "for n in seq:\n",
    "    print(n.subs(a,65))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 634$"
      ],
      "text/plain": [
       "634"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(seq[4:]).subs(a,65)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "22.0"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "374/17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 362$"
      ],
      "text/plain": [
       "362"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(seq[:4]).subs(a,65)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "eq=Eq(a,ln((x-2)/(x-1)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle a = \\log{\\left(\\frac{x - 2}{x - 1} \\right)}$"
      ],
      "text/plain": [
       "Eq(a, log((x - 2)/(x - 1)))"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left\\{\\frac{e^{a} - 2}{e^{a} - 1}\\right\\}$"
      ],
      "text/plain": [
       "{(exp(a) - 2)/(exp(a) - 1)}"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solveset(eq,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "answer,=_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{e^{a} - 2}{e^{a} - 1}$"
      ],
      "text/plain": [
       "(exp(a) - 2)/(exp(a) - 1)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{-2 + e^{2}}{-1 + e^{2}}$"
      ],
      "text/plain": [
       "(-2 + exp(2))/(-1 + exp(2))"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "answer.subs(a,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 新洲一中试题验证"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x,y=symbols(\"x,y\",real=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x0,y0=symbols(\"x0,y0\",real=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "eq=Eq(x0-a,2*p*y0**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle - a + x_{0} = 2 p y_{0}^{2}$"
      ],
      "text/plain": [
       "Eq(-a + x0, 2*p*y0**2)"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "r=x+y*I"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle x + i y$"
      ],
      "text/plain": [
       "x + I*y"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "theta=symbols(\"theta\",real=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "t=cos(theta)+sin(theta)*I"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle i \\sin{\\left(\\theta \\right)} + \\cos{\\left(\\theta \\right)}$"
      ],
      "text/plain": [
       "I*sin(theta) + cos(theta)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(x*cos(theta) - y*sin(theta), x*sin(theta) + y*cos(theta))"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(r*t).as_real_imag()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data=dict(zip((x0,y0),_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{x0: x*cos(theta) - y*sin(theta), y0: x*sin(theta) + y*cos(theta)}"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle - a + x \\cos{\\left(\\theta \\right)} - y \\sin{\\left(\\theta \\right)} = 2 p \\left(x \\sin{\\left(\\theta \\right)} + y \\cos{\\left(\\theta \\right)}\\right)^{2}$"
      ],
      "text/plain": [
       "Eq(-a + x*cos(theta) - y*sin(theta), 2*p*(x*sin(theta) + y*cos(theta))**2)"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eq.subs(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "check=_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle - a - 2 p \\left(x \\sin{\\left(\\theta \\right)} + y \\cos{\\left(\\theta \\right)}\\right)^{2} + x \\cos{\\left(\\theta \\right)} - y \\sin{\\left(\\theta \\right)}$"
      ],
      "text/plain": [
       "-a - 2*p*(x*sin(theta) + y*cos(theta))**2 + x*cos(theta) - y*sin(theta)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simplify_equation(check,expr=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle - a - 2 p x^{2} \\sin^{2}{\\left(\\theta \\right)} - 4 p x y \\sin{\\left(\\theta \\right)} \\cos{\\left(\\theta \\right)} - 2 p y^{2} \\cos^{2}{\\left(\\theta \\right)} + x \\cos{\\left(\\theta \\right)} - y \\sin{\\left(\\theta \\right)}$"
      ],
      "text/plain": [
       "-a - 2*p*x**2*sin(theta)**2 - 4*p*x*y*sin(theta)*cos(theta) - 2*p*y**2*cos(theta)**2 + x*cos(theta) - y*sin(theta)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expand(_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "check=_.subs(theta,pi/4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle - a - p x^{2} - 2 p x y - p y^{2} + \\frac{\\sqrt{2} x}{2} - \\frac{\\sqrt{2} y}{2}$"
      ],
      "text/plain": [
       "-a - p*x**2 - 2*p*x*y - p*y**2 + sqrt(2)*x/2 - sqrt(2)*y/2"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "P=Point(x,(2-sqrt(x))**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\operatorname{Point2D}\\left(x, \\left(2 - \\sqrt{x}\\right)^{2}\\right)$"
      ],
      "text/plain": [
       "Point2D(x, (2 - sqrt(x))**2)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "l=Line(x+y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "F=Point(2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle - \\sqrt{\\left(x - 2\\right)^{2} + \\left(\\left(2 - \\sqrt{x}\\right)^{2} - 2\\right)^{2}} + \\sqrt{\\left(- 2 \\sqrt{x} + x + 2\\right)^{2} + \\left(2 \\sqrt{x} + \\left(2 - \\sqrt{x}\\right)^{2} - 2\\right)^{2}}$"
      ],
      "text/plain": [
       "-sqrt((x - 2)**2 + ((2 - sqrt(x))**2 - 2)**2) + sqrt((-2*sqrt(x) + x + 2)**2 + (2*sqrt(x) + (2 - sqrt(x))**2 - 2)**2)"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P.distance(l)-P.distance(F)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "check=simplify(_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 0$"
      ],
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "check.subs(x,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sympy.core.numbers.Zero"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(check)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\emptyset$"
      ],
      "text/plain": [
       "EmptySet"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "function_range(pow(2,x),x,Reals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/huangzhiming/Documents/math_readproof_work-_ipython_files/2024'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tkinter import filedialog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-15 22:31:44.283 python[17520:7633427] +[CATransaction synchronize] called within transaction\n"
     ]
    }
   ],
   "source": [
    "image=filedialog.askopenfilename(title=\"choose a picture with red drawes.\",initialdir=\"./\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "image='/Users/huangzhiming/Downloads/scan-20240809134404-20-2.jpg'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2. 转换颜色空间到HSV\n",
    "img=cv2.imread(image)\n",
    "hsv_image = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# 3. 定义红色和蓝色的HSV范围\n",
    "lower_red1 = np.array([0, 70, 50])\n",
    "upper_red1 = np.array([10, 255, 255])\n",
    "lower_red2 = np.array([170, 70, 50])\n",
    "upper_red2 = np.array([180, 255, 255])\n",
    "\n",
    "lower_blue = np.array([100, 150, 0])\n",
    "upper_blue = np.array([140, 255, 255])\n",
    "\n",
    "# 4. 创建掩码，识别红色和蓝色\n",
    "red_mask1 = cv2.inRange(hsv_image, lower_red1, upper_red1)\n",
    "red_mask2 = cv2.inRange(hsv_image, lower_red2, upper_red2)\n",
    "blue_mask = cv2.inRange(hsv_image, lower_blue, upper_blue)\n",
    "\n",
    "# 5. 合并红色掩码（如果你认为红色可能跨越两个色调）\n",
    "red_mask = cv2.bitwise_or(red_mask1, red_mask2)\n",
    "\n",
    "# 6. 应用掩码到原始图像，提取红色和蓝色区域\n",
    "red_image = cv2.bitwise_and(img, img, mask=red_mask)\n",
    "blue_image = cv2.bitwise_and(img, img, mask=blue_mask)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 7. 可选：形态学操作去除噪声\n",
    "kernel = np.ones((3, 3), np.uint8)\n",
    "red_image = cv2.morphologyEx(red_image, cv2.MORPH_OPEN, kernel)\n",
    "blue_image = cv2.morphologyEx(blue_image, cv2.MORPH_OPEN, kernel)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m在当前单元格或上一个单元格中执行代码时 Kernel 崩溃。\n",
      "\u001b[1;31m请查看单元格中的代码，以确定故障的可能原因。\n",
      "\u001b[1;31m单击<a href='https://aka.ms/vscodeJupyterKernelCrash'>此处</a>了解详细信息。\n",
      "\u001b[1;31m有关更多详细信息，请查看 Jupyter <a href='command:jupyter.viewOutput'>log</a>。"
     ]
    }
   ],
   "source": [
    "\n",
    "# 8. 显示结果\n",
    "cv2.imshow('Red Mask', red_image)\n",
    "# 9. 等待按键，然后关闭所有窗口\n",
    "cv2.waitKey(0)\n",
    "cv2.destroyAllWindows()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "cv2.imshow('Blue Mask', blue_image)\n",
    "cv2.imshow('Original Image', img)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sympy.functions.combinatorial.numbers import nC,nP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "p=symbols(\"p\",real=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x,y=symbols(\"x,y\",integer=True,positive=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sympy.plotting import plot3d\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function plot in module matplotlib.pyplot:\n",
      "\n",
      "plot(*args: 'float | ArrayLike | str', scalex: 'bool' = True, scaley: 'bool' = True, data=None, **kwargs) -> 'list[Line2D]'\n",
      "    Plot y versus x as lines and/or markers.\n",
      "    \n",
      "    Call signatures::\n",
      "    \n",
      "        plot([x], y, [fmt], *, data=None, **kwargs)\n",
      "        plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)\n",
      "    \n",
      "    The coordinates of the points or line nodes are given by *x*, *y*.\n",
      "    \n",
      "    The optional parameter *fmt* is a convenient way for defining basic\n",
      "    formatting like color, marker and linestyle. It's a shortcut string\n",
      "    notation described in the *Notes* section below.\n",
      "    \n",
      "    >>> plot(x, y)        # plot x and y using default line style and color\n",
      "    >>> plot(x, y, 'bo')  # plot x and y using blue circle markers\n",
      "    >>> plot(y)           # plot y using x as index array 0..N-1\n",
      "    >>> plot(y, 'r+')     # ditto, but with red plusses\n",
      "    \n",
      "    You can use `.Line2D` properties as keyword arguments for more\n",
      "    control on the appearance. Line properties and *fmt* can be mixed.\n",
      "    The following two calls yield identical results:\n",
      "    \n",
      "    >>> plot(x, y, 'go--', linewidth=2, markersize=12)\n",
      "    >>> plot(x, y, color='green', marker='o', linestyle='dashed',\n",
      "    ...      linewidth=2, markersize=12)\n",
      "    \n",
      "    When conflicting with *fmt*, keyword arguments take precedence.\n",
      "    \n",
      "    \n",
      "    **Plotting labelled data**\n",
      "    \n",
      "    There's a convenient way for plotting objects with labelled data (i.e.\n",
      "    data that can be accessed by index ``obj['y']``). Instead of giving\n",
      "    the data in *x* and *y*, you can provide the object in the *data*\n",
      "    parameter and just give the labels for *x* and *y*::\n",
      "    \n",
      "    >>> plot('xlabel', 'ylabel', data=obj)\n",
      "    \n",
      "    All indexable objects are supported. This could e.g. be a `dict`, a\n",
      "    `pandas.DataFrame` or a structured numpy array.\n",
      "    \n",
      "    \n",
      "    **Plotting multiple sets of data**\n",
      "    \n",
      "    There are various ways to plot multiple sets of data.\n",
      "    \n",
      "    - The most straight forward way is just to call `plot` multiple times.\n",
      "      Example:\n",
      "    \n",
      "      >>> plot(x1, y1, 'bo')\n",
      "      >>> plot(x2, y2, 'go')\n",
      "    \n",
      "    - If *x* and/or *y* are 2D arrays a separate data set will be drawn\n",
      "      for every column. If both *x* and *y* are 2D, they must have the\n",
      "      same shape. If only one of them is 2D with shape (N, m) the other\n",
      "      must have length N and will be used for every data set m.\n",
      "    \n",
      "      Example:\n",
      "    \n",
      "      >>> x = [1, 2, 3]\n",
      "      >>> y = np.array([[1, 2], [3, 4], [5, 6]])\n",
      "      >>> plot(x, y)\n",
      "    \n",
      "      is equivalent to:\n",
      "    \n",
      "      >>> for col in range(y.shape[1]):\n",
      "      ...     plot(x, y[:, col])\n",
      "    \n",
      "    - The third way is to specify multiple sets of *[x]*, *y*, *[fmt]*\n",
      "      groups::\n",
      "    \n",
      "      >>> plot(x1, y1, 'g^', x2, y2, 'g-')\n",
      "    \n",
      "      In this case, any additional keyword argument applies to all\n",
      "      datasets. Also, this syntax cannot be combined with the *data*\n",
      "      parameter.\n",
      "    \n",
      "    By default, each line is assigned a different style specified by a\n",
      "    'style cycle'. The *fmt* and line property parameters are only\n",
      "    necessary if you want explicit deviations from these defaults.\n",
      "    Alternatively, you can also change the style cycle using\n",
      "    :rc:`axes.prop_cycle`.\n",
      "    \n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    x, y : array-like or scalar\n",
      "        The horizontal / vertical coordinates of the data points.\n",
      "        *x* values are optional and default to ``range(len(y))``.\n",
      "    \n",
      "        Commonly, these parameters are 1D arrays.\n",
      "    \n",
      "        They can also be scalars, or two-dimensional (in that case, the\n",
      "        columns represent separate data sets).\n",
      "    \n",
      "        These arguments cannot be passed as keywords.\n",
      "    \n",
      "    fmt : str, optional\n",
      "        A format string, e.g. 'ro' for red circles. See the *Notes*\n",
      "        section for a full description of the format strings.\n",
      "    \n",
      "        Format strings are just an abbreviation for quickly setting\n",
      "        basic line properties. All of these and more can also be\n",
      "        controlled by keyword arguments.\n",
      "    \n",
      "        This argument cannot be passed as keyword.\n",
      "    \n",
      "    data : indexable object, optional\n",
      "        An object with labelled data. If given, provide the label names to\n",
      "        plot in *x* and *y*.\n",
      "    \n",
      "        .. note::\n",
      "            Technically there's a slight ambiguity in calls where the\n",
      "            second label is a valid *fmt*. ``plot('n', 'o', data=obj)``\n",
      "            could be ``plt(x, y)`` or ``plt(y, fmt)``. In such cases,\n",
      "            the former interpretation is chosen, but a warning is issued.\n",
      "            You may suppress the warning by adding an empty format string\n",
      "            ``plot('n', 'o', '', data=obj)``.\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    list of `.Line2D`\n",
      "        A list of lines representing the plotted data.\n",
      "    \n",
      "    Other Parameters\n",
      "    ----------------\n",
      "    scalex, scaley : bool, default: True\n",
      "        These parameters determine if the view limits are adapted to the\n",
      "        data limits. The values are passed on to\n",
      "        `~.axes.Axes.autoscale_view`.\n",
      "    \n",
      "    **kwargs : `~matplotlib.lines.Line2D` properties, optional\n",
      "        *kwargs* are used to specify properties like a line label (for\n",
      "        auto legends), linewidth, antialiasing, marker face color.\n",
      "        Example::\n",
      "    \n",
      "        >>> plot([1, 2, 3], [1, 2, 3], 'go-', label='line 1', linewidth=2)\n",
      "        >>> plot([1, 2, 3], [1, 4, 9], 'rs', label='line 2')\n",
      "    \n",
      "        If you specify multiple lines with one plot call, the kwargs apply\n",
      "        to all those lines. In case the label object is iterable, each\n",
      "        element is used as labels for each set of data.\n",
      "    \n",
      "        Here is a list of available `.Line2D` properties:\n",
      "    \n",
      "        Properties:\n",
      "        agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array and two offsets from the bottom left corner of the image\n",
      "        alpha: scalar or None\n",
      "        animated: bool\n",
      "        antialiased or aa: bool\n",
      "        clip_box: `~matplotlib.transforms.BboxBase` or None\n",
      "        clip_on: bool\n",
      "        clip_path: Patch or (Path, Transform) or None\n",
      "        color or c: color\n",
      "        dash_capstyle: `.CapStyle` or {'butt', 'projecting', 'round'}\n",
      "        dash_joinstyle: `.JoinStyle` or {'miter', 'round', 'bevel'}\n",
      "        dashes: sequence of floats (on/off ink in points) or (None, None)\n",
      "        data: (2, N) array or two 1D arrays\n",
      "        drawstyle or ds: {'default', 'steps', 'steps-pre', 'steps-mid', 'steps-post'}, default: 'default'\n",
      "        figure: `~matplotlib.figure.Figure`\n",
      "        fillstyle: {'full', 'left', 'right', 'bottom', 'top', 'none'}\n",
      "        gapcolor: color or None\n",
      "        gid: str\n",
      "        in_layout: bool\n",
      "        label: object\n",
      "        linestyle or ls: {'-', '--', '-.', ':', '', (offset, on-off-seq), ...}\n",
      "        linewidth or lw: float\n",
      "        marker: marker style string, `~.path.Path` or `~.markers.MarkerStyle`\n",
      "        markeredgecolor or mec: color\n",
      "        markeredgewidth or mew: float\n",
      "        markerfacecolor or mfc: color\n",
      "        markerfacecoloralt or mfcalt: color\n",
      "        markersize or ms: float\n",
      "        markevery: None or int or (int, int) or slice or list[int] or float or (float, float) or list[bool]\n",
      "        mouseover: bool\n",
      "        path_effects: list of `.AbstractPathEffect`\n",
      "        picker: float or callable[[Artist, Event], tuple[bool, dict]]\n",
      "        pickradius: float\n",
      "        rasterized: bool\n",
      "        sketch_params: (scale: float, length: float, randomness: float)\n",
      "        snap: bool or None\n",
      "        solid_capstyle: `.CapStyle` or {'butt', 'projecting', 'round'}\n",
      "        solid_joinstyle: `.JoinStyle` or {'miter', 'round', 'bevel'}\n",
      "        transform: unknown\n",
      "        url: str\n",
      "        visible: bool\n",
      "        xdata: 1D array\n",
      "        ydata: 1D array\n",
      "        zorder: float\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    scatter : XY scatter plot with markers of varying size and/or color (\n",
      "        sometimes also called bubble chart).\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    **Format Strings**\n",
      "    \n",
      "    A format string consists of a part for color, marker and line::\n",
      "    \n",
      "        fmt = '[marker][line][color]'\n",
      "    \n",
      "    Each of them is optional. If not provided, the value from the style\n",
      "    cycle is used. Exception: If ``line`` is given, but no ``marker``,\n",
      "    the data will be a line without markers.\n",
      "    \n",
      "    Other combinations such as ``[color][marker][line]`` are also\n",
      "    supported, but note that their parsing may be ambiguous.\n",
      "    \n",
      "    **Markers**\n",
      "    \n",
      "    =============   ===============================\n",
      "    character       description\n",
      "    =============   ===============================\n",
      "    ``'.'``         point marker\n",
      "    ``','``         pixel marker\n",
      "    ``'o'``         circle marker\n",
      "    ``'v'``         triangle_down marker\n",
      "    ``'^'``         triangle_up marker\n",
      "    ``'<'``         triangle_left marker\n",
      "    ``'>'``         triangle_right marker\n",
      "    ``'1'``         tri_down marker\n",
      "    ``'2'``         tri_up marker\n",
      "    ``'3'``         tri_left marker\n",
      "    ``'4'``         tri_right marker\n",
      "    ``'8'``         octagon marker\n",
      "    ``'s'``         square marker\n",
      "    ``'p'``         pentagon marker\n",
      "    ``'P'``         plus (filled) marker\n",
      "    ``'*'``         star marker\n",
      "    ``'h'``         hexagon1 marker\n",
      "    ``'H'``         hexagon2 marker\n",
      "    ``'+'``         plus marker\n",
      "    ``'x'``         x marker\n",
      "    ``'X'``         x (filled) marker\n",
      "    ``'D'``         diamond marker\n",
      "    ``'d'``         thin_diamond marker\n",
      "    ``'|'``         vline marker\n",
      "    ``'_'``         hline marker\n",
      "    =============   ===============================\n",
      "    \n",
      "    **Line Styles**\n",
      "    \n",
      "    =============    ===============================\n",
      "    character        description\n",
      "    =============    ===============================\n",
      "    ``'-'``          solid line style\n",
      "    ``'--'``         dashed line style\n",
      "    ``'-.'``         dash-dot line style\n",
      "    ``':'``          dotted line style\n",
      "    =============    ===============================\n",
      "    \n",
      "    Example format strings::\n",
      "    \n",
      "        'b'    # blue markers with default shape\n",
      "        'or'   # red circles\n",
      "        '-g'   # green solid line\n",
      "        '--'   # dashed line with default color\n",
      "        '^k:'  # black triangle_up markers connected by a dotted line\n",
      "    \n",
      "    **Colors**\n",
      "    \n",
      "    The supported color abbreviations are the single letter codes\n",
      "    \n",
      "    =============    ===============================\n",
      "    character        color\n",
      "    =============    ===============================\n",
      "    ``'b'``          blue\n",
      "    ``'g'``          green\n",
      "    ``'r'``          red\n",
      "    ``'c'``          cyan\n",
      "    ``'m'``          magenta\n",
      "    ``'y'``          yellow\n",
      "    ``'k'``          black\n",
      "    ``'w'``          white\n",
      "    =============    ===============================\n",
      "    \n",
      "    and the ``'CN'`` colors that index into the default property cycle.\n",
      "    \n",
      "    If the color is the only part of the format string, you can\n",
      "    additionally use any  `matplotlib.colors` spec, e.g. full names\n",
      "    (``'green'``) or hex strings (``'#008000'``).\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(plt.plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "x,y=symbols(\"x,y\",real=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "expr=binomial(x,y)*p**y*(1-p)**(x-y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "a7=np.array([7.0]*100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "x7 = np.linspace(10, 20, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# 准备数据\n",
    "np_x = np.linspace(8, 20, 100)\n",
    "np_y = np.linspace(2, 8, 100)\n",
    "X, Y = np.meshgrid(np_x, np_y)\n",
    "f=lambdify([x,y],expand_func(expr).subs(p,1/S(1.3)))\n",
    "Z = f(X,Y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# 创建新的图形和3D坐标轴\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111, projection='3d')\n",
    "\n",
    "# 绘制3D曲面图\n",
    "ax.scatter3D(np_x,a7,f(x7,a7))\n",
    "surf = ax.plot_surface(X, Y, Z, cmap='viridis')\n",
    "\n",
    "# 自定义轴标签和标题\n",
    "ax.set_xlabel('X axis: n')\n",
    "ax.set_ylabel('Y axis: k')\n",
    "ax.set_zlabel('Z axis: P')\n",
    "ax.set_title('3D plot of a function Z = f(X, Y)')\n",
    "\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<sympy.plotting.plot.Plot at 0x113eef810>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot(expr.subs(p,1/S(2)),expr.subs(p,0.7),expr.subs(p,0.8),(x,5,20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "expr1=expand_func(expr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{x \\left(1 - p\\right)^{8 - x} \\left(1 - p\\right)^{x - 7}}{x - 7}$"
      ],
      "text/plain": [
       "x*(1 - p)**(8 - x)*(1 - p)**(x - 7)/(x - 7)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expr1/expr1.subs(x,x-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{x \\left(1 - p\\right)}{x - 7}$"
      ],
      "text/plain": [
       "x*(1 - p)/(x - 7)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simplify(_)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle p^{y} \\left(1 - p\\right)^{x - y} {\\binom{x}{y}}$"
      ],
      "text/plain": [
       "p**y*(1 - p)**(x - y)*binomial(x, y)"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n",
      "823543/4194304    823543/16777216\n",
      "9\n",
      "13176688/129140163    1647086/43046721\n",
      "10\n",
      "66706983/1000000000    66706983/2000000000\n",
      "11\n",
      "1264962048/25937424601    790601280/25937424601\n",
      "12\n",
      "28309290625/743008370688    28309290625/990677827584\n",
      "13\n",
      "724552190208/23298085122481    633983166432/23298085122481\n"
     ]
    }
   ],
   "source": [
    "for i in range(8,14):\n",
    "    t=S(7)/S(i)\n",
    "    n=floor(7/t)\n",
    "    print(n)\n",
    "    delta1=expr.subs(p,t).subs({x:n,y:7})-expr.subs(p,t).subs({x:n,y:6})\n",
    "    delta2=expr.subs(p,t).subs({x:n,y:7})-expr.subs(p,t).subs({x:n,y:8})\n",
    "    print(delta1 ,\"  \",delta2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 0.243615368491502$"
      ],
      "text/plain": [
       "0.243615368491502"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expr.subs(p,0.63).subs({x:,y:7})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle 0.200305969648568$"
      ],
      "text/plain": [
       "0.200305969648568"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expr.subs(p,0.63).subs({x:11,y:6})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{\\left(1 - p\\right)^{x - y} \\left(1 - p\\right)^{- x + y + 1} {\\binom{x}{y}}}{{\\binom{x - 1}{y}}}$"
      ],
      "text/plain": [
       "(1 - p)**(x - y)*(1 - p)**(-x + y + 1)*binomial(x, y)/binomial(x - 1, y)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expand_func(expr/expr.subs(x,x-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\frac{- p x + x}{x - y}$"
      ],
      "text/plain": [
       "(-p*x + x)/(x - y)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simplify(_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle \\left\\{\\frac{y}{p}\\right\\} \\setminus \\left\\{y\\right\\}$"
      ],
      "text/plain": [
       "Complement({y/p}, {y})"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solveset(_-1,x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
